Overview

Dataset statistics

Number of variables33
Number of observations867550
Missing cells1280777
Missing cells (%)4.5%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory218.4 MiB
Average record size in memory264.0 B

Variable types

Categorical16
Numeric15
Unsupported1
Boolean1

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
Accident_Index has a high cardinality: 558085 distinct values High cardinality
Date has a high cardinality: 2190 distinct values High cardinality
Time has a high cardinality: 1439 distinct values High cardinality
Local_Authority_(Highway) has a high cardinality: 207 distinct values High cardinality
LSOA_of_Accident_Location has a high cardinality: 34140 distinct values High cardinality
Location_Easting_OSGR is highly correlated with LongitudeHigh correlation
Location_Northing_OSGR is highly correlated with LatitudeHigh correlation
Longitude is highly correlated with Location_Easting_OSGRHigh correlation
Latitude is highly correlated with Location_Northing_OSGRHigh correlation
Police_Force is highly correlated with Local_Authority_(District)High correlation
Local_Authority_(District) is highly correlated with Police_ForceHigh correlation
Speed_limit is highly correlated with Urban_or_Rural_AreaHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Location_Easting_OSGR is highly correlated with LongitudeHigh correlation
Location_Northing_OSGR is highly correlated with LatitudeHigh correlation
Longitude is highly correlated with Location_Easting_OSGRHigh correlation
Latitude is highly correlated with Location_Northing_OSGRHigh correlation
Police_Force is highly correlated with Local_Authority_(District)High correlation
Local_Authority_(District) is highly correlated with Police_ForceHigh correlation
Speed_limit is highly correlated with Urban_or_Rural_AreaHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Location_Easting_OSGR is highly correlated with LongitudeHigh correlation
Location_Northing_OSGR is highly correlated with LatitudeHigh correlation
Longitude is highly correlated with Location_Easting_OSGRHigh correlation
Latitude is highly correlated with Location_Northing_OSGRHigh correlation
Police_Force is highly correlated with Local_Authority_(District)High correlation
Local_Authority_(District) is highly correlated with Police_ForceHigh correlation
Speed_limit is highly correlated with Urban_or_Rural_AreaHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Location_Easting_OSGR is highly correlated with Location_Northing_OSGR and 4 other fieldsHigh correlation
Location_Northing_OSGR is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Longitude is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Latitude is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Police_Force is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
Local_Authority_(District) is highly correlated with Location_Easting_OSGR and 4 other fieldsHigh correlation
1st_Road_Class is highly correlated with Road_Type and 1 other fieldsHigh correlation
1st_Road_Number is highly correlated with 2nd_Road_NumberHigh correlation
Road_Type is highly correlated with 1st_Road_ClassHigh correlation
Speed_limit is highly correlated with 1st_Road_Class and 1 other fieldsHigh correlation
Junction_Control is highly correlated with Pedestrian_Crossing-Physical_FacilitiesHigh correlation
2nd_Road_Number is highly correlated with 1st_Road_NumberHigh correlation
Pedestrian_Crossing-Physical_Facilities is highly correlated with Junction_ControlHigh correlation
Weather_Conditions is highly correlated with Road_Surface_ConditionsHigh correlation
Road_Surface_Conditions is highly correlated with Weather_ConditionsHigh correlation
Urban_or_Rural_Area is highly correlated with Speed_limitHigh correlation
Junction_Detail has 867550 (100.0%) missing values Missing
Junction_Control has 350857 (40.4%) missing values Missing
LSOA_of_Accident_Location has 58613 (6.8%) missing values Missing
Junction_Detail is an unsupported type, check if it needs cleaning or further analysis Unsupported
1st_Road_Number has 236889 (27.3%) zeros Zeros
2nd_Road_Number has 669484 (77.2%) zeros Zeros

Reproduction

Analysis started2022-01-09 14:09:31.884097
Analysis finished2022-01-09 14:17:04.640856
Duration7 minutes and 32.76 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Accident_Index
Categorical

HIGH CARDINALITY

Distinct558085
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2.01E+12
137503 
2.00913E+12
 
6255
2.01013E+12
 
5760
2.01113E+12
 
5402
2.00946E+12
 
5158
Other values (558080)
707472 

Length

Max length255
Median length13
Mean length11.82327474
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique557880 ?
Unique (%)64.3%

Sample

1st row200901BS70001
2nd row200901BS70002
3rd row200901BS70003
4th row200901BS70004
5th row200901BS70005

Common Values

ValueCountFrequency (%)
2.01E+12137503
 
15.8%
2.00913E+126255
 
0.7%
2.01013E+125760
 
0.7%
2.01113E+125402
 
0.6%
2.00946E+125158
 
0.6%
2.01144E+125000
 
0.6%
2.01146E+124867
 
0.6%
2.01046E+124837
 
0.6%
2.01044E+124809
 
0.6%
2.00944E+124683
 
0.5%
Other values (558075)683276
78.8%

Length

2022-01-09T17:17:06.003625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.01e+12137503
 
15.8%
2.00913e+126255
 
0.7%
2.01013e+125760
 
0.7%
2.01113e+125402
 
0.6%
2.00946e+125158
 
0.6%
2.01144e+125000
 
0.6%
2.01146e+124867
 
0.6%
2.01046e+124837
 
0.6%
2.01044e+124809
 
0.6%
2.00944e+124683
 
0.5%
Other values (558075)683276
78.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Location_Easting_OSGR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60961
Distinct (%)7.0%
Missing86
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean439345.3646
Minimum64950
Maximum655370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:06.712507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum64950
5-th percentile263690
Q1375220
median439790
Q3523520
95-th percentile577090
Maximum655370
Range590420
Interquartile range (IQR)148300

Descriptive statistics

Standard deviation95453.94719
Coefficient of variation (CV)0.2172640362
Kurtosis-0.4165415443
Mean439345.3646
Median Absolute Deviation (MAD)77234.5
Skewness-0.3256436201
Sum3.811162873 × 1011
Variance9111456034
MonotonicityNot monotonic
2022-01-09T17:17:06.971510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
533650222
 
< 0.1%
531170201
 
< 0.1%
531180177
 
< 0.1%
530980169
 
< 0.1%
533470159
 
< 0.1%
532440157
 
< 0.1%
530020156
 
< 0.1%
529210156
 
< 0.1%
534950150
 
< 0.1%
531000149
 
< 0.1%
Other values (60951)865768
99.8%
ValueCountFrequency (%)
649501
< 0.1%
653501
< 0.1%
656701
< 0.1%
656901
< 0.1%
658601
< 0.1%
659501
< 0.1%
667101
< 0.1%
682901
< 0.1%
688801
< 0.1%
737601
< 0.1%
ValueCountFrequency (%)
6553702
 
< 0.1%
6552901
 
< 0.1%
6552802
 
< 0.1%
6552302
 
< 0.1%
6551801
 
< 0.1%
6551601
 
< 0.1%
6551502
 
< 0.1%
6551401
 
< 0.1%
6551204
< 0.1%
6551105
< 0.1%

Location_Northing_OSGR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct84093
Distinct (%)9.7%
Missing86
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean299358.2346
Minimum10520
Maximum1205100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:07.181507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10520
5-th percentile104210
Q1178530
median266360
Q3396920
95-th percentile662830
Maximum1205100
Range1194580
Interquartile range (IQR)218390

Descriptive statistics

Standard deviation161335.2572
Coefficient of variation (CV)0.5389370947
Kurtosis0.8529571153
Mean299358.2346
Median Absolute Deviation (MAD)98140
Skewness1.028136542
Sum2.596824917 × 1011
Variance2.602906523 × 1010
MonotonicityNot monotonic
2022-01-09T17:17:07.380507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181310237
 
< 0.1%
181170205
 
< 0.1%
181110192
 
< 0.1%
179660188
 
< 0.1%
181060184
 
< 0.1%
181050179
 
< 0.1%
178620179
 
< 0.1%
181120175
 
< 0.1%
180820170
 
< 0.1%
181370170
 
< 0.1%
Other values (84083)865585
99.8%
ValueCountFrequency (%)
105201
< 0.1%
105302
< 0.1%
105601
< 0.1%
106501
< 0.1%
106701
< 0.1%
108401
< 0.1%
108601
< 0.1%
111001
< 0.1%
116201
< 0.1%
124601
< 0.1%
ValueCountFrequency (%)
12051001
< 0.1%
12039001
< 0.1%
11980001
< 0.1%
11917001
< 0.1%
11915001
< 0.1%
11910301
< 0.1%
11896001
< 0.1%
11861001
< 0.1%
11838301
< 0.1%
11786001
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct665752
Distinct (%)76.7%
Missing86
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-1.440834649
Minimum-7.516225
Maximum1.759398
Zeros0
Zeros (%)0.0%
Negative755392
Negative (%)87.1%
Memory size6.6 MiB
2022-01-09T17:17:07.676115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-7.516225
5-th percentile-4.0650028
Q1-2.37124325
median-1.405855
Q3-0.2147575
95-th percentile0.54888005
Maximum1.759398
Range9.275623
Interquartile range (IQR)2.15648575

Descriptive statistics

Standard deviation1.403280097
Coefficient of variation (CV)-0.9739355579
Kurtosis-0.3706435719
Mean-1.440834649
Median Absolute Deviation (MAD)1.117442
Skewness-0.3658417901
Sum-1249872.188
Variance1.969195031
MonotonicityNot monotonic
2022-01-09T17:17:07.957114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.31059643
 
< 0.1%
-0.97761142
 
< 0.1%
-3.24169438
 
< 0.1%
-1.23439333
 
< 0.1%
-1.87104331
 
< 0.1%
-1.21669430
 
< 0.1%
-0.17344530
 
< 0.1%
-1.99996729
 
< 0.1%
-1.26536829
 
< 0.1%
-0.11506329
 
< 0.1%
Other values (665742)867130
> 99.9%
(Missing)86
 
< 0.1%
ValueCountFrequency (%)
-7.5162251
< 0.1%
-7.5074681
< 0.1%
-7.5072071
< 0.1%
-7.5041211
< 0.1%
-7.4989731
< 0.1%
-7.4974611
< 0.1%
-7.491831
< 0.1%
-7.4656091
< 0.1%
-7.4602591
< 0.1%
-7.4500151
< 0.1%
ValueCountFrequency (%)
1.7593981
< 0.1%
1.7593821
< 0.1%
1.7583371
< 0.1%
1.758191
< 0.1%
1.7581061
< 0.1%
1.7579151
< 0.1%
1.7579071
< 0.1%
1.756511
< 0.1%
1.756431
< 0.1%
1.7563471
< 0.1%

Latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct638427
Distinct (%)73.6%
Missing86
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean52.58216458
Minimum49.914488
Maximum60.724682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:08.241112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum49.914488
5-th percentile50.824713
Q151.49274875
median52.2885245
Q353.4673965
95-th percentile55.838885
Maximum60.724682
Range10.810194
Interquartile range (IQR)1.97464775

Descriptive statistics

Standard deviation1.452734597
Coefficient of variation (CV)0.02762789644
Kurtosis0.8237475072
Mean52.58216458
Median Absolute Deviation (MAD)0.889849
Skewness1.019095565
Sum45613134.81
Variance2.110437811
MonotonicityNot monotonic
2022-01-09T17:17:08.498132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.50669344
 
< 0.1%
52.94971942
 
< 0.1%
52.45879840
 
< 0.1%
51.52695638
 
< 0.1%
52.98985733
 
< 0.1%
51.56010832
 
< 0.1%
51.48207632
 
< 0.1%
52.95505830
 
< 0.1%
53.79242330
 
< 0.1%
52.9388630
 
< 0.1%
Other values (638417)867113
99.9%
(Missing)86
 
< 0.1%
ValueCountFrequency (%)
49.9144881
< 0.1%
49.9145131
< 0.1%
49.9147011
< 0.1%
49.9148041
< 0.1%
49.915731
< 0.1%
49.9159871
< 0.1%
49.9177031
< 0.1%
49.9179131
< 0.1%
49.9208951
< 0.1%
49.9252251
< 0.1%
ValueCountFrequency (%)
60.7246821
< 0.1%
60.7147741
< 0.1%
60.6620431
< 0.1%
60.6057431
< 0.1%
60.6041171
< 0.1%
60.5980551
< 0.1%
60.5865861
< 0.1%
60.5552341
< 0.1%
60.5356231
< 0.1%
60.489821
< 0.1%

Police_Force
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.53854533
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:08.762132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median30
Q346
95-th percentile94
Maximum98
Range97
Interquartile range (IQR)39

Descriptive statistics

Standard deviation25.63716814
Coefficient of variation (CV)0.839501943
Kurtosis0.308705958
Mean30.53854533
Median Absolute Deviation (MAD)18
Skewness0.8503239169
Sum26493715
Variance657.2643903
MonotonicityNot monotonic
2022-01-09T17:17:09.059140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1131752
 
15.2%
2036445
 
4.2%
634731
 
4.0%
1331857
 
3.7%
4331680
 
3.7%
4425852
 
3.0%
4625749
 
3.0%
5024890
 
2.9%
9724442
 
2.8%
424022
 
2.8%
Other values (41)476130
54.9%
ValueCountFrequency (%)
1131752
15.2%
37617
 
0.9%
424022
 
2.8%
518625
 
2.1%
634731
 
4.0%
716838
 
1.9%
1020033
 
2.3%
117922
 
0.9%
1211790
 
1.4%
1331857
 
3.7%
ValueCountFrequency (%)
982000
 
0.2%
9724442
2.8%
962988
 
0.3%
9512299
1.4%
942967
 
0.3%
934382
 
0.5%
925721
 
0.7%
913384
 
0.4%
637956
 
0.9%
6215966
1.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
3
741295 
2
114777 
1
 
11478

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3741295
85.4%
2114777
 
13.2%
111478
 
1.3%

Length

2022-01-09T17:17:09.270130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:09.400133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3741295
85.4%
2114777
 
13.2%
111478
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number_of_Vehicles
Real number (ℝ≥0)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.831130194
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:09.581494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum34
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7152484902
Coefficient of variation (CV)0.3906049349
Kurtosis22.46128485
Mean1.831130194
Median Absolute Deviation (MAD)0
Skewness1.883800766
Sum1588597
Variance0.5115804028
MonotonicityNot monotonic
2022-01-09T17:17:09.973498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2514099
59.3%
1263876
30.4%
369977
 
8.1%
414614
 
1.7%
53281
 
0.4%
61019
 
0.1%
7346
 
< 0.1%
8165
 
< 0.1%
987
 
< 0.1%
1031
 
< 0.1%
Other values (14)55
 
< 0.1%
ValueCountFrequency (%)
1263876
30.4%
2514099
59.3%
369977
 
8.1%
414614
 
1.7%
53281
 
0.4%
61019
 
0.1%
7346
 
< 0.1%
8165
 
< 0.1%
987
 
< 0.1%
1031
 
< 0.1%
ValueCountFrequency (%)
341
< 0.1%
321
< 0.1%
291
< 0.1%
221
< 0.1%
202
< 0.1%
191
< 0.1%
182
< 0.1%
171
< 0.1%
162
< 0.1%
152
< 0.1%

Number_of_Casualties
Real number (ℝ≥0)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.355902253
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:10.183496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum87
Range86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8227544818
Coefficient of variation (CV)0.6067948333
Kurtosis304.8817278
Mean1.355902253
Median Absolute Deviation (MAD)0
Skewness7.439683896
Sum1176313
Variance0.6769249374
MonotonicityNot monotonic
2022-01-09T17:17:10.380499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1662552
76.4%
2141269
 
16.3%
340173
 
4.6%
414757
 
1.7%
55351
 
0.6%
62032
 
0.2%
7700
 
0.1%
8301
 
< 0.1%
9132
 
< 0.1%
1083
 
< 0.1%
Other values (29)200
 
< 0.1%
ValueCountFrequency (%)
1662552
76.4%
2141269
 
16.3%
340173
 
4.6%
414757
 
1.7%
55351
 
0.6%
62032
 
0.2%
7700
 
0.1%
8301
 
< 0.1%
9132
 
< 0.1%
1083
 
< 0.1%
ValueCountFrequency (%)
871
< 0.1%
631
< 0.1%
512
< 0.1%
481
< 0.1%
451
< 0.1%
432
< 0.1%
421
< 0.1%
411
< 0.1%
401
< 0.1%
362
< 0.1%

Date
Categorical

HIGH CARDINALITY

Distinct2190
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
21/10/2005
 
822
18/11/2005
 
787
29/09/2006
 
784
22/09/2006
 
780
7/12/2005
 
775
Other values (2185)
863602 

Length

Max length10
Median length10
Mean length9.413184255
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/1/2009
2nd row5/1/2009
3rd row4/1/2009
4th row5/1/2009
5th row6/1/2009

Common Values

ValueCountFrequency (%)
21/10/2005822
 
0.1%
18/11/2005787
 
0.1%
29/09/2006784
 
0.1%
22/09/2006780
 
0.1%
7/12/2005775
 
0.1%
1/12/2006750
 
0.1%
30/09/2005749
 
0.1%
9/12/2005748
 
0.1%
12/10/2005746
 
0.1%
19/12/2005744
 
0.1%
Other values (2180)859865
99.1%

Length

2022-01-09T17:17:10.569496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21/10/2005822
 
0.1%
18/11/2005787
 
0.1%
29/09/2006784
 
0.1%
22/09/2006780
 
0.1%
7/12/2005775
 
0.1%
1/12/2006750
 
0.1%
30/09/2005749
 
0.1%
9/12/2005748
 
0.1%
12/10/2005746
 
0.1%
6/10/2006744
 
0.1%
Other values (2180)859865
99.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Day_of_Week
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.126450349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:10.740497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.926804174
Coefficient of variation (CV)0.4669398663
Kurtosis-1.188983492
Mean4.126450349
Median Absolute Deviation (MAD)2
Skewness-0.07424872214
Sum3579902
Variance3.712574326
MonotonicityNot monotonic
2022-01-09T17:17:10.891497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6143407
16.5%
4130810
15.1%
5130039
15.0%
3128417
14.8%
2122253
14.1%
7117274
13.5%
195350
11.0%
ValueCountFrequency (%)
195350
11.0%
2122253
14.1%
3128417
14.8%
4130810
15.1%
5130039
15.0%
6143407
16.5%
7117274
13.5%
ValueCountFrequency (%)
7117274
13.5%
6143407
16.5%
5130039
15.0%
4130810
15.1%
3128417
14.8%
2122253
14.1%
195350
11.0%

Time
Categorical

HIGH CARDINALITY

Distinct1439
Distinct (%)0.2%
Missing84
Missing (%)< 0.1%
Memory size6.6 MiB
17:00
 
8531
17:30
 
8034
16:00
 
7722
15:30
 
7684
18:00
 
7620
Other values (1434)
827875 

Length

Max length5
Median length5
Mean length4.764025334
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15:11
2nd row10:59
3rd row14:19
4th row8:10
5th row17:25

Common Values

ValueCountFrequency (%)
17:008531
 
1.0%
17:308034
 
0.9%
16:007722
 
0.9%
15:307684
 
0.9%
18:007620
 
0.9%
16:307413
 
0.9%
15:006776
 
0.8%
8:306774
 
0.8%
14:006220
 
0.7%
13:006205
 
0.7%
Other values (1429)794487
91.6%

Length

2022-01-09T17:17:11.096495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17:008531
 
1.0%
17:308034
 
0.9%
16:007722
 
0.9%
15:307684
 
0.9%
18:007620
 
0.9%
16:307413
 
0.9%
15:006776
 
0.8%
8:306774
 
0.8%
14:006220
 
0.7%
13:006205
 
0.7%
Other values (1429)794487
91.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Local_Authority_(District)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct416
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.8887787
Minimum1
Maximum941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:11.247500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q1112
median323
Q3531
95-th percentile919
Maximum941
Range940
Interquartile range (IQR)419

Descriptive statistics

Standard deviation260.5982614
Coefficient of variation (CV)0.7426805221
Kurtosis-0.5253929141
Mean350.8887787
Median Absolute Deviation (MAD)209
Skewness0.5124034513
Sum304413560
Variance67911.45385
MonotonicityNot monotonic
2022-01-09T17:17:11.415533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30016566
 
1.9%
20411304
 
1.3%
1028873
 
1.0%
18861
 
1.0%
9267944
 
0.9%
2007829
 
0.9%
917564
 
0.9%
2157453
 
0.9%
96522
 
0.8%
9236401
 
0.7%
Other values (406)778233
89.7%
ValueCountFrequency (%)
18861
1.0%
24940
0.6%
34127
0.5%
44806
0.6%
54954
0.6%
63701
0.4%
74298
0.5%
85328
0.6%
96522
0.8%
104323
0.5%
ValueCountFrequency (%)
941190
 
< 0.1%
9402134
0.2%
9391229
 
0.1%
9383079
0.4%
9371230
 
0.1%
936195
 
< 0.1%
9351900
0.2%
9341821
0.2%
933147
 
< 0.1%
9323352
0.4%

Local_Authority_(Highway)
Categorical

HIGH CARDINALITY

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
E10000016
 
22579
E10000030
 
21430
E10000017
 
19282
E10000012
 
18446
E10000014
 
17107
Other values (202)
768706 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE09000020
2nd rowE09000020
3rd rowE09000020
4th rowE09000020
5th rowE09000020

Common Values

ValueCountFrequency (%)
E1000001622579
 
2.6%
E1000003021430
 
2.5%
E1000001719282
 
2.2%
E1000001218446
 
2.1%
E1000001417107
 
2.0%
E0800002516566
 
1.9%
E1000001515744
 
1.8%
E1000002813659
 
1.6%
E1000001912099
 
1.4%
E1000002411668
 
1.3%
Other values (197)698970
80.6%

Length

2022-01-09T17:17:11.641501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e1000001622579
 
2.6%
e1000003021430
 
2.5%
e1000001719282
 
2.2%
e1000001218446
 
2.1%
e1000001417107
 
2.0%
e0800002516566
 
1.9%
e1000001515744
 
1.8%
e1000002813659
 
1.6%
e1000001912099
 
1.4%
e1000002411668
 
1.3%
Other values (197)698970
80.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1st_Road_Class
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.091393003
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:11.746495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.431849803
Coefficient of variation (CV)0.3499663322
Kurtosis-1.063104444
Mean4.091393003
Median Absolute Deviation (MAD)1
Skewness0.1056560506
Sum3549488
Variance2.050193858
MonotonicityNot monotonic
2022-01-09T17:17:11.867495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3393461
45.4%
6250413
28.9%
4109910
 
12.7%
577749
 
9.0%
133792
 
3.9%
22225
 
0.3%
ValueCountFrequency (%)
133792
 
3.9%
22225
 
0.3%
3393461
45.4%
4109910
 
12.7%
577749
 
9.0%
6250413
28.9%
ValueCountFrequency (%)
6250413
28.9%
577749
 
9.0%
4109910
 
12.7%
3393461
45.4%
22225
 
0.3%
133792
 
3.9%

1st_Road_Number
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6329
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.772014
Minimum-1
Maximum9999
Zeros236889
Zeros (%)27.3%
Negative2
Negative (%)< 0.1%
Memory size6.6 MiB
2022-01-09T17:17:12.075496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median124
Q3709
95-th percentile5305
Maximum9999
Range10000
Interquartile range (IQR)709

Descriptive statistics

Standard deviation1822.998663
Coefficient of variation (CV)1.817959254
Kurtosis3.400867697
Mean1002.772014
Median Absolute Deviation (MAD)124
Skewness2.085518023
Sum869954861
Variance3323324.127
MonotonicityNot monotonic
2022-01-09T17:17:12.342498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0236889
27.3%
111247
 
1.3%
69533
 
1.1%
48273
 
1.0%
255892
 
0.7%
55743
 
0.7%
405632
 
0.6%
385198
 
0.6%
34957
 
0.6%
234372
 
0.5%
Other values (6319)569814
65.7%
ValueCountFrequency (%)
-12
 
< 0.1%
0236889
27.3%
111247
 
1.3%
23796
 
0.4%
34957
 
0.6%
48273
 
1.0%
55743
 
0.7%
69533
 
1.1%
71048
 
0.1%
82483
 
0.3%
ValueCountFrequency (%)
9999172
< 0.1%
99631
 
< 0.1%
99621
 
< 0.1%
99501
 
< 0.1%
99101
 
< 0.1%
98752
 
< 0.1%
98551
 
< 0.1%
98541
 
< 0.1%
98531
 
< 0.1%
98402
 
< 0.1%

Road_Type
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Single carriageway
648718 
Dual carriageway
128858 
Roundabout
 
57246
One way street
 
18528
Slip road
 
8932

Length

Max length18
Median length18
Mean length16.93016887
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOne way street
2nd rowSingle carriageway
3rd rowSingle carriageway
4th rowSingle carriageway
5th rowSingle carriageway

Common Values

ValueCountFrequency (%)
Single carriageway648718
74.8%
Dual carriageway128858
 
14.9%
Roundabout57246
 
6.6%
One way street18528
 
2.1%
Slip road8932
 
1.0%
Unknown5268
 
0.6%

Length

2022-01-09T17:17:12.596499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:12.692496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
carriageway777576
46.0%
single648718
38.4%
dual128858
 
7.6%
roundabout57246
 
3.4%
one18528
 
1.1%
way18528
 
1.1%
street18528
 
1.1%
slip8932
 
0.5%
road8932
 
0.5%
unknown5268
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Speed_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.16950608
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:12.975499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q130
median30
Q350
95-th percentile70
Maximum70
Range60
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.19067928
Coefficient of variation (CV)0.3622889514
Kurtosis-0.4574719998
Mean39.16950608
Median Absolute Deviation (MAD)0
Skewness1.088795917
Sum33981505
Variance201.3753785
MonotonicityNot monotonic
2022-01-09T17:17:13.258502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
30558871
64.4%
60141734
 
16.3%
4069886
 
8.1%
7063639
 
7.3%
5026430
 
3.0%
206973
 
0.8%
1010
 
< 0.1%
157
 
< 0.1%
ValueCountFrequency (%)
1010
 
< 0.1%
157
 
< 0.1%
206973
 
0.8%
30558871
64.4%
4069886
 
8.1%
5026430
 
3.0%
60141734
 
16.3%
7063639
 
7.3%
ValueCountFrequency (%)
7063639
 
7.3%
60141734
 
16.3%
5026430
 
3.0%
4069886
 
8.1%
30558871
64.4%
206973
 
0.8%
157
 
< 0.1%
1010
 
< 0.1%

Junction_Detail
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing867550
Missing (%)100.0%
Memory size6.6 MiB

Junction_Control
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing350857
Missing (%)40.4%
Memory size6.6 MiB
Giveway or uncontrolled
419677 
Automatic traffic signal
89956 
Stop Sign
 
5572
Authorised person
 
1488

Length

Max length24
Median length23
Mean length23.00584486
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGiveway or uncontrolled
2nd rowGiveway or uncontrolled
3rd rowGiveway or uncontrolled
4th rowAutomatic traffic signal
5th rowAutomatic traffic signal

Common Values

ValueCountFrequency (%)
Giveway or uncontrolled419677
48.4%
Automatic traffic signal89956
 
10.4%
Stop Sign5572
 
0.6%
Authorised person1488
 
0.2%
(Missing)350857
40.4%

Length

2022-01-09T17:17:13.549498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:13.709500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
giveway419677
27.2%
or419677
27.2%
uncontrolled419677
27.2%
automatic89956
 
5.8%
traffic89956
 
5.8%
signal89956
 
5.8%
stop5572
 
0.4%
sign5572
 
0.4%
authorised1488
 
0.1%
person1488
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

2nd_Road_Class
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.642198144
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Negative358733
Negative (%)41.4%
Memory size6.6 MiB
2022-01-09T17:17:13.817496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median3
Q36
95-th percentile6
Maximum6
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.205662107
Coefficient of variation (CV)1.213255756
Kurtosis-1.822779175
Mean2.642198144
Median Absolute Deviation (MAD)3
Skewness-0.1343789554
Sum2292239
Variance10.27626955
MonotonicityNot monotonic
2022-01-09T17:17:13.940497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-1358733
41.4%
6340699
39.3%
387176
 
10.0%
540535
 
4.7%
433825
 
3.9%
15889
 
0.7%
2693
 
0.1%
ValueCountFrequency (%)
-1358733
41.4%
15889
 
0.7%
2693
 
0.1%
387176
 
10.0%
433825
 
3.9%
540535
 
4.7%
6340699
39.3%
ValueCountFrequency (%)
6340699
39.3%
540535
 
4.7%
433825
 
3.9%
387176
 
10.0%
2693
 
0.1%
15889
 
0.7%
-1358733
41.4%

2nd_Road_Number
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6670
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.5278935
Minimum-1
Maximum9999
Zeros669484
Zeros (%)77.2%
Negative9152
Negative (%)1.1%
Memory size6.6 MiB
2022-01-09T17:17:14.102533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile3408
Maximum9999
Range10000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1305.163869
Coefficient of variation (CV)3.420887153
Kurtosis17.0034429
Mean381.5278935
Median Absolute Deviation (MAD)0
Skewness4.095138019
Sum330994524
Variance1703452.725
MonotonicityNot monotonic
2022-01-09T17:17:14.348495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0669484
77.2%
-19152
 
1.1%
12119
 
0.2%
41713
 
0.2%
61675
 
0.2%
401219
 
0.1%
51084
 
0.1%
31075
 
0.1%
381050
 
0.1%
72011042
 
0.1%
Other values (6660)177937
 
20.5%
ValueCountFrequency (%)
-19152
 
1.1%
0669484
77.2%
12119
 
0.2%
2576
 
0.1%
31075
 
0.1%
41713
 
0.2%
51084
 
0.1%
61675
 
0.2%
7197
 
< 0.1%
8517
 
0.1%
ValueCountFrequency (%)
9999386
< 0.1%
99651
 
< 0.1%
99621
 
< 0.1%
99321
 
< 0.1%
99311
 
< 0.1%
98981
 
< 0.1%
98741
 
< 0.1%
98621
 
< 0.1%
98381
 
< 0.1%
98323
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing17
Missing (%)< 0.1%
Memory size6.6 MiB
None within 50 metres
862339 
Control by other authorised person
 
3084
Control by school crossing patrol
 
2110

Length

Max length34
Median length21
Mean length21.07540001
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone within 50 metres
2nd rowNone within 50 metres
3rd rowNone within 50 metres
4th rowNone within 50 metres
5th rowNone within 50 metres

Common Values

ValueCountFrequency (%)
None within 50 metres862339
99.4%
Control by other authorised person3084
 
0.4%
Control by school crossing patrol2110
 
0.2%
(Missing)17
 
< 0.1%

Length

2022-01-09T17:17:14.529501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:14.653499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none862339
24.8%
within862339
24.8%
50862339
24.8%
metres862339
24.8%
control5194
 
0.1%
by5194
 
0.1%
other3084
 
0.1%
authorised3084
 
0.1%
person3084
 
0.1%
school2110
 
0.1%
Other values (2)4220
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Pedestrian_Crossing-Physical_Facilities
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing32
Missing (%)< 0.1%
Memory size6.6 MiB
No physical crossing within 50 meters
726922 
Pedestrian phase at traffic signal junction
 
56776
non-junction pedestrian crossing
 
44229
Zebra crossing
 
22689
Central refuge
 
14530

Length

Max length43
Median length37
Mean length36.10451426
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo physical crossing within 50 meters
2nd rowZebra crossing
3rd rowNo physical crossing within 50 meters
4th rowPedestrian phase at traffic signal junction
5th rowNo physical crossing within 50 meters

Common Values

ValueCountFrequency (%)
No physical crossing within 50 meters726922
83.8%
Pedestrian phase at traffic signal junction56776
 
6.5%
non-junction pedestrian crossing44229
 
5.1%
Zebra crossing22689
 
2.6%
Central refuge14530
 
1.7%
Footbridge or subway2372
 
0.3%
(Missing)32
 
< 0.1%

Length

2022-01-09T17:17:14.824495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:14.976498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
crossing793840
16.1%
no726922
14.8%
physical726922
14.8%
within726922
14.8%
50726922
14.8%
meters726922
14.8%
pedestrian101005
 
2.1%
junction56776
 
1.2%
signal56776
 
1.2%
traffic56776
 
1.2%
Other values (9)216646
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Light_Conditions
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Daylight: Street light present
635472 
Darkness: Street lights present and lit
172065 
Darkeness: No street lighting
 
48736
Darkness: Street lighting unknown
 
7605
Darkness: Street lights present but unlit
 
3672

Length

Max length41
Median length30
Mean length31.80168982
Min length29

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDaylight: Street light present
2nd rowDaylight: Street light present
3rd rowDaylight: Street light present
4th rowDaylight: Street light present
5th rowDarkness: Street lights present and lit

Common Values

ValueCountFrequency (%)
Daylight: Street light present635472
73.2%
Darkness: Street lights present and lit172065
 
19.8%
Darkeness: No street lighting48736
 
5.6%
Darkness: Street lighting unknown7605
 
0.9%
Darkness: Street lights present but unlit3672
 
0.4%

Length

2022-01-09T17:17:15.149534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:15.284498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
street867550
22.7%
present811209
21.2%
daylight635472
16.6%
light635472
16.6%
darkness183342
 
4.8%
lights175737
 
4.6%
and172065
 
4.5%
lit172065
 
4.5%
lighting56341
 
1.5%
darkeness48736
 
1.3%
Other values (4)63685
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Weather_Conditions
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing107
Missing (%)< 0.1%
Memory size6.6 MiB
Fine without high winds
695558 
Raining without high winds
98743 
Other
 
21313
Unknown
 
16236
Fine with high winds
 
10860
Other values (4)
 
24733

Length

Max length26
Median length23
Mean length22.52128612
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFine without high winds
2nd rowFine without high winds
3rd rowFine without high winds
4th rowOther
5th rowFine without high winds

Common Values

ValueCountFrequency (%)
Fine without high winds695558
80.2%
Raining without high winds98743
 
11.4%
Other21313
 
2.5%
Unknown16236
 
1.9%
Fine with high winds10860
 
1.3%
Raining with high winds10784
 
1.2%
Snowing without high winds7928
 
0.9%
Fog or mist4940
 
0.6%
Snowing with high winds1081
 
0.1%
(Missing)107
 
< 0.1%

Length

2022-01-09T17:17:15.434499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:15.529533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
high824954
24.6%
winds824954
24.6%
without802229
23.9%
fine706418
21.1%
raining109527
 
3.3%
with22725
 
0.7%
other21313
 
0.6%
unknown16236
 
0.5%
snowing9009
 
0.3%
fog4940
 
0.1%
Other values (2)9880
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Road_Surface_Conditions
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing1004
Missing (%)0.1%
Memory size6.6 MiB
Dry
598491 
Wet/Damp
239197 
Frost/Ice
 
20733
Snow
 
7149
Flood (Over 3cm of water)
 
976

Length

Max length25
Median length3
Mean length4.556759826
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDry
2nd rowWet/Damp
3rd rowDry
4th rowFrost/Ice
5th rowDry

Common Values

ValueCountFrequency (%)
Dry598491
69.0%
Wet/Damp239197
 
27.6%
Frost/Ice20733
 
2.4%
Snow7149
 
0.8%
Flood (Over 3cm of water)976
 
0.1%
(Missing)1004
 
0.1%

Length

2022-01-09T17:17:15.683533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:15.774496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
dry598491
68.8%
wet/damp239197
 
27.5%
frost/ice20733
 
2.4%
snow7149
 
0.8%
flood976
 
0.1%
over976
 
0.1%
3cm976
 
0.1%
of976
 
0.1%
water976
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct8
Distinct (%)< 0.1%
Missing13
Missing (%)< 0.1%
Memory size6.6 MiB
None
845879 
Roadworks
 
10311
Ol or diesel
 
3107
Mud
 
2607
Road surface defective
 
2163
Other values (3)
 
3470

Length

Max length47
Median length4
Mean length4.250086163
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None845879
97.5%
Roadworks10311
 
1.2%
Ol or diesel3107
 
0.4%
Mud2607
 
0.3%
Road surface defective2163
 
0.2%
Auto traffic singal out1655
 
0.2%
Permanent sign or marking defective or obscured1336
 
0.2%
Auto traffic signal partly defective479
 
0.1%
(Missing)13
 
< 0.1%

Length

2022-01-09T17:17:15.984498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:16.088537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none845879
94.7%
roadworks10311
 
1.2%
or5779
 
0.6%
defective3978
 
0.4%
ol3107
 
0.3%
diesel3107
 
0.3%
mud2607
 
0.3%
road2163
 
0.2%
surface2163
 
0.2%
traffic2134
 
0.2%
Other values (9)11746
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct6
Distinct (%)< 0.1%
Missing26
Missing (%)< 0.1%
Memory size6.6 MiB
None
851446 
Other object in carriageway
 
7095
Any animal (except a ridden horse)
 
4686
Pedestrian in carriageway (not injured)
 
1952
Involvement with previous accident
 
1395

Length

Max length39
Median length4
Mean length4.51328263
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None851446
98.1%
Other object in carriageway7095
 
0.8%
Any animal (except a ridden horse)4686
 
0.5%
Pedestrian in carriageway (not injured)1952
 
0.2%
Involvement with previous accident1395
 
0.2%
Dislodged vehicle load in carriageway950
 
0.1%
(Missing)26
 
< 0.1%

Length

2022-01-09T17:17:16.254534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:16.377501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none851446
91.7%
in9997
 
1.1%
carriageway9997
 
1.1%
object7095
 
0.8%
other7095
 
0.8%
a4686
 
0.5%
ridden4686
 
0.5%
horse4686
 
0.5%
except4686
 
0.5%
animal4686
 
0.5%
Other values (11)18972
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Urban_or_Rural_Area
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
1
559614 
2
307819 
3
 
117

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1559614
64.5%
2307819
35.5%
3117
 
< 0.1%

Length

2022-01-09T17:17:16.526498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T17:17:16.614495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1559614
64.5%
2307819
35.5%
3117
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing2130
Missing (%)0.2%
Memory size1.7 MiB
True
699364 
False
166056 
(Missing)
 
2130
ValueCountFrequency (%)
True699364
80.6%
False166056
 
19.1%
(Missing)2130
 
0.2%
2022-01-09T17:17:16.684494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

LSOA_of_Accident_Location
Categorical

HIGH CARDINALITY
MISSING

Distinct34140
Distinct (%)4.2%
Missing58613
Missing (%)6.8%
Memory size6.6 MiB
E01000004
 
1749
E01011365
 
921
E01004736
 
812
E01004764
 
702
E01008440
 
676
Other values (34135)
804077 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique580 ?
Unique (%)0.1%

Sample

1st rowE01002882
2nd rowE01002886
3rd rowE01002912
4th rowE01002871
5th rowE01002840

Common Values

ValueCountFrequency (%)
E010000041749
 
0.2%
E01011365921
 
0.1%
E01004736812
 
0.1%
E01004764702
 
0.1%
E01008440676
 
0.1%
E01005131592
 
0.1%
E01002444522
 
0.1%
E01001771455
 
0.1%
E01018648448
 
0.1%
E01023722441
 
0.1%
Other values (34130)801619
92.4%
(Missing)58613
 
6.8%

Length

2022-01-09T17:17:16.832495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e010000041749
 
0.2%
e01011365921
 
0.1%
e01004736812
 
0.1%
e01004764702
 
0.1%
e01008440676
 
0.1%
e01005131592
 
0.1%
e01002444522
 
0.1%
e01001771455
 
0.1%
e01018648448
 
0.1%
e01023722441
 
0.1%
Other values (34130)801619
99.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Year
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.93322
Minimum2005
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2022-01-09T17:17:16.983501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12006
median2009
Q32010
95-th percentile2011
Maximum2011
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.326594241
Coefficient of variation (CV)0.001158701005
Kurtosis-1.670757708
Mean2007.93322
Median Absolute Deviation (MAD)2
Skewness-0.04206633452
Sum1741982465
Variance5.413040762
MonotonicityNot monotonic
2022-01-09T17:17:17.120496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2005198735
22.9%
2006189161
21.8%
2009163554
18.9%
2010154414
17.8%
2011151474
17.5%
200710212
 
1.2%
ValueCountFrequency (%)
2005198735
22.9%
2006189161
21.8%
200710212
 
1.2%
2009163554
18.9%
2010154414
17.8%
2011151474
17.5%
ValueCountFrequency (%)
2011151474
17.5%
2010154414
17.8%
2009163554
18.9%
200710212
 
1.2%
2006189161
21.8%
2005198735
22.9%

Interactions

2022-01-09T17:16:31.274036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:22.118815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:35.974231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:44.439230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:53.306627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:02.317627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:10.755625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:19.024625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:26.903671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:35.967672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:47.206989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:55.777004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:16:04.877036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:16:13.748025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:16:22.178043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:16:31.848046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:23.219499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:36.512230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:45.136626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:14:54.021627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:02.919626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:11.287624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:15:19.554623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-01-09T17:16:13.204023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:16:21.577021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-01-09T17:16:30.586038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-01-09T17:17:17.369498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-09T17:17:18.712498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-09T17:17:19.479894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-09T17:17:19.921899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-09T17:17:20.454893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-09T17:16:39.871058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-09T17:16:44.003185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-09T17:16:57.867216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-09T17:17:00.471819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Accident_IndexLocation_Easting_OSGRLocation_Northing_OSGRLongitudeLatitudePolice_ForceAccident_SeverityNumber_of_VehiclesNumber_of_CasualtiesDateDay_of_WeekTimeLocal_Authority_(District)Local_Authority_(Highway)1st_Road_Class1st_Road_NumberRoad_TypeSpeed_limitJunction_DetailJunction_Control2nd_Road_Class2nd_Road_NumberPedestrian_Crossing-Human_ControlPedestrian_Crossing-Physical_FacilitiesLight_ConditionsWeather_ConditionsRoad_Surface_ConditionsSpecial_Conditions_at_SiteCarriageway_HazardsUrban_or_Rural_AreaDid_Police_Officer_Attend_Scene_of_AccidentLSOA_of_Accident_LocationYear
0200901BS70001524910.0180800.0-0.20134951.51227312211/1/2009515:1112E0900002060One way street30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028822009
1200901BS70002525050.0181040.0-0.19924851.514399122115/1/2009210:5912E0900002050Single carriageway30NaNGiveway or uncontrolled50None within 50 metresZebra crossingDaylight: Street light presentFine without high windsWet/DampNoneNone1YesE010028862009
2200901BS70003526490.0177990.0-0.17959951.48666813214/1/2009114:1912E090000203308Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010029122009
3200901BS70004524800.0180300.0-0.20311051.50780412215/1/200928:1012E090000203402Single carriageway30NaNAutomatic traffic signal4450None within 50 metresPedestrian phase at traffic signal junctionDaylight: Street light presentOtherFrost/IceNoneNone1YesE010028712009
4200901BS70005526930.0177490.0-0.17344551.48207612216/1/2009317:2512E0900002033212Single carriageway30NaNAutomatic traffic signal33220None within 50 metresNo physical crossing within 50 metersDarkness: Street lights present and litFine without high windsDryNoneNone1YesE010028402009
5200901BS70006526060.0178730.0-0.18552551.49341513231/1/2009511:4812E0900002060Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028392009
6200901BS70007526580.0177270.0-0.17856151.48017712218/1/2009513:5812E0900002033220Single carriageway30NaNGiveway or uncontrolled33220None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028412009
7200901BS70008526550.0178580.0-0.17852451.49195713112/1/2009613:1812E0900002050Dual carriageway30NaNAutomatic traffic signal33218None within 50 metresPedestrian phase at traffic signal junctionDaylight: Street light presentFine without high windsDryNoneNone1YesE010028352009
8200901BS70009527310.0179100.0-0.16739551.49646013127/1/2009412:1512E0900002060Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010028192009
9200901BS70010526250.0177370.0-0.18327551.481150131110/1/200979:5212E090000203308Single carriageway30NaNAutomatic traffic signal33220None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentOtherWet/DampRoadworksNone1YesE010028432009

Last rows

Accident_IndexLocation_Easting_OSGRLocation_Northing_OSGRLongitudeLatitudePolice_ForceAccident_SeverityNumber_of_VehiclesNumber_of_CasualtiesDateDay_of_WeekTimeLocal_Authority_(District)Local_Authority_(Highway)1st_Road_Class1st_Road_NumberRoad_TypeSpeed_limitJunction_DetailJunction_Control2nd_Road_Class2nd_Road_NumberPedestrian_Crossing-Human_ControlPedestrian_Crossing-Physical_FacilitiesLight_ConditionsWeather_ConditionsRoad_Surface_ConditionsSpecial_Conditions_at_SiteCarriageway_HazardsUrban_or_Rural_AreaDid_Police_Officer_Attend_Scene_of_AccidentLSOA_of_Accident_LocationYear
867540200701MD68578533950.0171420.0-0.07470451.425910122131/12/200721:158E090000283212Dual carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDarkness: Street lights present and litFine without high windsWet/DampNoneNone1YesE010039482007
867541200701MD68647532660.0180170.0-0.08996751.504847132115/12/2007713:408E0900002833Single carriageway30NaNAutomatic traffic signal33200None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010039292007
867542200701MD68794534840.0177530.0-0.05958351.480607122112/7/200758:298E0900002832Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentOtherDryNoneNone1YesE010039882007
867543200701MD68800532460.0176980.0-0.09404351.47622613215/7/200750:018E090000283215Single carriageway30NaNAutomatic traffic signal60None within 50 metresPedestrian phase at traffic signal junctionDarkness: Street lights present and litRaining without high windsWet/DampNoneNone1NoE010039212007
867544200701MD68803535180.0177340.0-0.05476251.478818131120/07/2007612:308E0900002850Dual carriageway30NaNGiveway or uncontrolled32None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentRaining without high windsWet/DampNoneNone1YesE010039842007
867545200701MD68807531780.0178620.0-0.10321751.491123122111/5/200767:538E0900002833Single carriageway30NaNAutomatic traffic signal33204None within 50 metresPedestrian phase at traffic signal junctionDaylight: Street light presentFine without high windsDryNoneNone1NoE010031102007
867546200701MD68808532110.0177000.0-0.09907251.476488132115/05/200739:008E090000283202Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsDryNoneNone1YesE010039192007
867547200701MD68812532390.0173070.0-0.09651351.441104132117/05/2007520:559E0900002250Single carriageway30NaNGiveway or uncontrolled60None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentUnknownDryNoneNone1NoE010031702007
867548200701MD68824531950.0180250.0-0.10016151.50573213218/5/2007317:508E0900002833200Single carriageway30NaNNaN-10None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine with high windsDryNoneNone1YesE010039272007
867549200701MD68851533630.0179100.0-0.07640451.495003121114/11/2007410:458E0900002832206Single carriageway30NaNGiveway or uncontrolled50None within 50 metresZebra crossingDaylight: Street light presentFine without high windsDryNoneNone1YesE010039782007

Duplicate rows

Most frequently occurring

Accident_IndexLocation_Easting_OSGRLocation_Northing_OSGRLongitudeLatitudePolice_ForceAccident_SeverityNumber_of_VehiclesNumber_of_CasualtiesDateDay_of_WeekTimeLocal_Authority_(District)Local_Authority_(Highway)1st_Road_Class1st_Road_NumberRoad_TypeSpeed_limitJunction_Control2nd_Road_Class2nd_Road_NumberPedestrian_Crossing-Human_ControlPedestrian_Crossing-Physical_FacilitiesLight_ConditionsWeather_ConditionsRoad_Surface_ConditionsSpecial_Conditions_at_SiteCarriageway_HazardsUrban_or_Rural_AreaDid_Police_Officer_Attend_Scene_of_AccidentLSOA_of_Accident_LocationYear# duplicates
02.0111E+12429320.0557990.0-1.54413554.9157781031118/01/201138:25150E080000242194Dual carriageway70Giveway or uncontrolled3182None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsFrost/IceNoneNone2YesE0100887320112
12.01E+12503250.0487860.0-0.41580154.275973123214/2/2005613:40186E1000002360Single carriageway30Giveway or uncontrolled364None within 50 metresNo physical crossing within 50 metersDaylight: Street light presentFine without high windsWet/DampNoneNone1YesE0102782620052